DistOS 2015W Session 9

BONIC

Public Resource Computing Platform
Gives scientists the ability to use large amounts of computation resources.
The clients do not connect directly with each other but instead they talk to a central server located at Berkley
The goals of Bonic are:

1) reduce the barriers of entry
2) Share resources among autonomous projects
3) Support diverse applications
4) Reward participants.

A BONIC application can be identified by a single master URL, 
which serves as the homepage as well as the directory of the servers.

SETI@Home

Uses public resource computing to analyze radio signals to find extraterrestrial intelligence
Need good quality telescope to search for radio signals, and lots of computational power, which was unavailable locally
It has not yet found extraterrestrial intelligence, but its has established credibility of public resource computing projects which are given by the public
Uses BONIC as a backbone for the project
Uses relational database to store information on a large scale, further it uses a multi-threaded server to distribute work to clients

MapReduce

A programming model presented by Google to do large scale parallel computations
Uses the Map() and Reduce() functions from functional style programming languages

Map (Filtering)

Takes a function and applies it to all elements of the given data set

Reduce (Summary)

Accumulates results from the data set using a given function

Naiad

A programming model similar to MapReduce but with streaming capabilities so that data results are almost instantaneous
A distributed system for executing data parallel cyclic dataflow programs offering high throughput and low latency
Aims to provide a general purpose system which will fulfill the requirements and the will also support wide variety of high level programming models.
Real Time Applications:

Batch iterative Machine Learning:

VW, an open source distributed machine learning performs iteration in 3 phases: each process updates local state; processes independently training on local data; and process jointly performed global average which is All Reduce.